The traditional ant colony algorithm (ACA) is difficult to overcome the problems of suboptimal path and slow convergence in path planning. To solve these problems, a jump point optimization ant colony algorithm (JPOACA) is proposed. By introducing the value function of jump point search (JPS) algorithm, low-cost neighborhood nodes are selected, and then the multi neighborhood of ACA is used to expand the neighborhood of JPS algorithm, expand the vision of JPOACA, increase the number of lowcost neighborhoods, design angle heuristic information function and step size heuristic information function in the low-cost JPS algorithm neighborhood, improve the path optimization ability of the algorithm, and finally supplement pheromones at the jump points, In order to improve the convergence speed of the fusion algorithm, a pheromone supplement method is added to the hops of the optimal path. The simulation results show that the path planned by JPOACA is smooth and better, and the convergence speed and adaptability to complex terrain are significantly improved.